432 research outputs found
Cooperative Edge Caching in User-Centric Clustered Mobile Networks
With files proactively stored at base stations (BSs), mobile edge caching
enables direct content delivery without remote file fetching, which can reduce
the end-to-end delay while relieving backhaul pressure. To effectively utilize
the limited cache size in practice, cooperative caching can be leveraged to
exploit caching diversity, by allowing users served by multiple base stations
under the emerging user-centric network architecture. This paper explores
delay-optimal cooperative edge caching in large-scale user-centric mobile
networks, where the content placement and cluster size are optimized based on
the stochastic information of network topology, traffic distribution, channel
quality, and file popularity. Specifically, a greedy content placement
algorithm is proposed based on the optimal bandwidth allocation, which can
achieve (1-1/e)-optimality with linear computational complexity. In addition,
the optimal user-centric cluster size is studied, and a condition constraining
the maximal cluster size is presented in explicit form, which reflects the
tradeoff between caching diversity and spectrum efficiency. Extensive
simulations are conducted for analysis validation and performance evaluation.
Numerical results demonstrate that the proposed greedy content placement
algorithm can reduce the average file transmission delay up to 50% compared
with the non-cooperative and hit-ratio-maximal schemes. Furthermore, the
optimal clustering is also discussed considering the influences of different
system parameters.Comment: IEEE TM
Caching at the Wireless Edge: Design Aspects, Challenges and Future Directions
Caching at the wireless edge is a promising way of boosting spectral
efficiency and reducing energy consumption of wireless systems. These
improvements are rooted in the fact that popular contents are reused,
asynchronously, by many users. In this article, we first introduce methods to
predict the popularity distributions and user preferences, and the impact of
erroneous information. We then discuss the two aspects of caching systems,
namely content placement and delivery. We expound the key differences between
wired and wireless caching, and outline the differences in the system arising
from where the caching takes place, e.g., at base stations, or on the wireless
devices themselves. Special attention is paid to the essential limitations in
wireless caching, and possible tradeoffs between spectral efficiency, energy
efficiency and cache size.Comment: Published in IEEE Communications Magazin
Analysis of Cached-Enabled Hybrid Millimter Wave & Sub-6 GHz Massive MIMO Networks
This paper focuses on edge caching in mm/{\mu}Wave hybrid wireless networks,
in which all mmWave SBSs and {\mu}Wave MBSs are capable of storing contents to
alleviate the traffic burden on the backhaul link that connect the BSs and the
core network to retrieve the non-cached contents. The main aim of this work is
to address the effect of capacity-limited backhaul on the average success
probability (ASP) of file delivery and latency. In particular, we consider a
more practical mmWave hybrid beamforming in small cells and massive MIMO
communication in macro cells. Based on stochastic geometry and a simple
retransmission protocol, we derive the association probabilities by which the
ASP of file delivery and latency are derived. Taking no caching event as the
benchmark, we evaluate these QoS performance metrics under MC and UC placement
policies. The theoretical results demonstrate that backhaul capacity indeed has
a significant impact on network performance especially under weak backhaul
capacity. Besides, we also show the tradeoff among cache size, retransmission
attempts, ASP of file delivery, and latency. The interplay shows that cache
size and retransmission under different caching placement schemes alleviates
the backhaul requirements. Simulation results are present to valid our
analysis
Base Station ON-OFF Switching in 5G Wireless Networks: Approaches and Challenges
To achieve the expected 1000x data rates under the exponential growth of
traffic demand, a large number of base stations (BS) or access points (AP) will
be deployed in the fifth generation (5G) wireless systems, to support high data
rate services and to provide seamless coverage. Although such BSs are expected
to be small-scale with lower power, the aggregated energy consumption of all
BSs would be remarkable, resulting in increased environmental and economic
concerns. In existing cellular networks, turning off the under-utilized BSs is
an efficient approach to conserve energy while preserving the quality of
service (QoS) of mobile users. However, in 5G systems with new physical layer
techniques and the highly heterogeneous network architecture, new challenges
arise in the design of BS ON-OFF switching strategies. In this article, we
begin with a discussion on the inherent technical challenges of BS ON-OFF
switching. We then provide a comprehensive review of recent advances on
switching mechanisms in different application scenarios. Finally, we present
open research problems and conclude the paper.Comment: Appear to IEEE Wireless Communications, 201
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
As the explosive growth of smart devices and the advent of many new
applications, traffic volume has been growing exponentially. The traditional
centralized network architecture cannot accommodate such user demands due to
heavy burden on the backhaul links and long latency. Therefore, new
architectures which bring network functions and contents to the network edge
are proposed, i.e., mobile edge computing and caching. Mobile edge networks
provide cloud computing and caching capabilities at the edge of cellular
networks. In this survey, we make an exhaustive review on the state-of-the-art
research efforts on mobile edge networks. We first give an overview of mobile
edge networks including definition, architecture and advantages. Next, a
comprehensive survey of issues on computing, caching and communication
techniques at the network edge is presented respectively. The applications and
use cases of mobile edge networks are discussed. Subsequently, the key enablers
of mobile edge networks such as cloud technology, SDN/NFV and smart devices are
discussed. Finally, open research challenges and future directions are
presented as well
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
As a key technique for enabling artificial intelligence, machine learning
(ML) is capable of solving complex problems without explicit programming.
Motivated by its successful applications to many practical tasks like image
recognition, both industry and the research community have advocated the
applications of ML in wireless communication. This paper comprehensively
surveys the recent advances of the applications of ML in wireless
communication, which are classified as: resource management in the MAC layer,
networking and mobility management in the network layer, and localization in
the application layer. The applications in resource management further include
power control, spectrum management, backhaul management, cache management,
beamformer design and computation resource management, while ML based
networking focuses on the applications in clustering, base station switching
control, user association and routing. Moreover, literatures in each aspect is
organized according to the adopted ML techniques. In addition, several
conditions for applying ML to wireless communication are identified to help
readers decide whether to use ML and which kind of ML techniques to use, and
traditional approaches are also summarized together with their performance
comparison with ML based approaches, based on which the motivations of surveyed
literatures to adopt ML are clarified. Given the extensiveness of the research
area, challenges and unresolved issues are presented to facilitate future
studies, where ML based network slicing, infrastructure update to support ML
based paradigms, open data sets and platforms for researchers, theoretical
guidance for ML implementation and so on are discussed.Comment: 34 pages,8 figure
Energy Efficiency of Downlink Networks with Caching at Base Stations
Caching popular contents at base stations (BSs) can reduce the backhaul cost
and improve the network throughput. Yet whether locally caching at the BSs can
improve the energy efficiency (EE), a major goal for 5th generation cellular
networks, remains unclear. Due to the entangled impact of various factors on EE
such as interference level, backhaul capacity, BS density, power consumption
parameters, BS sleeping, content popularity and cache capacity, another
important question is what are the key factors that contribute more to the EE
gain from caching. In this paper, we attempt to explore the potential of EE of
the cache-enabled wireless access networks and identify the key factors. By
deriving closed-form expression of the approximated EE, we provide the
condition when the EE can benefit from caching, find the optimal cache capacity
that maximizes the network EE, and analyze the maximal EE gain brought by
caching. We show that caching at the BSs can improve the network EE when power
efficient cache hardware is used. When local caching has EE gain over not
caching, caching more contents at the BSs may not provide higher EE. Numerical
and simulation results show that the caching EE gain is large when the backhaul
capacity is stringent, interference level is low, content popularity is skewed,
and when caching at pico BSs instead of macro BSs.Comment: Accepted by Journal on Selected Areas in Communications (JSAC),
Special Issue on Energy-Efficient Techniques for 5G Wireless Communication
System
Learning-based Caching in Cloud-Aided Wireless Networks
This paper studies content caching in cloud-aided wireless networks where
small cell base stations with limited storage are connected to the cloud via
limited capacity fronthaul links. By formulating a utility (inverse of service
delay) maximization problem, we propose a cache update algorithm based on
spatio-temporal traffic demands. To account for the large number of contents,
we propose a content clustering algorithm to group similar contents.
Subsequently, with the aid of regret learning at small cell base stations and
the cloud, each base station caches contents based on the learned content
popularity subject to its storage constraints. The performance of the proposed
caching algorithm is evaluated for sparse and dense environments while
investigating the tradeoff between global and local class popularity.
Simulation results show 15% and 40% gains in the proposed method compared to
various baselines.Comment: 4 pages, 5 figures, Accepted, IEEE Comm Letter 201
Air-Ground Integrated Mobile Edge Networks: Architecture, Challenges and Opportunities
The ever-increasing mobile data demands have posed significant challenges in
the current radio access networks, while the emerging computation-heavy
Internet of things (IoT) applications with varied requirements demand more
flexibility and resilience from the cloud/edge computing architecture. In this
article, to address the issues, we propose a novel air-ground integrated mobile
edge network (AGMEN), where UAVs are flexibly deployed and scheduled, and
assist the communication, caching, and computing of the edge network. In
specific, we present the detailed architecture of AGMEN, and investigate the
benefits and application scenarios of drone-cells, and UAV-assisted edge
caching and computing. Furthermore, the challenging issues in AGMEN are
discussed, and potential research directions are highlighted.Comment: Accepted by IEEE Communications Magazine. 5 figure
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning in communications and networking. Modern networks, e.g.,
Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become
more decentralized and autonomous. In such networks, network entities need to
make decisions locally to maximize the network performance under uncertainty of
network environment. Reinforcement learning has been efficiently used to enable
the network entities to obtain the optimal policy including, e.g., decisions or
actions, given their states when the state and action spaces are small.
However, in complex and large-scale networks, the state and action spaces are
usually large, and the reinforcement learning may not be able to find the
optimal policy in reasonable time. Therefore, deep reinforcement learning, a
combination of reinforcement learning with deep learning, has been developed to
overcome the shortcomings. In this survey, we first give a tutorial of deep
reinforcement learning from fundamental concepts to advanced models. Then, we
review deep reinforcement learning approaches proposed to address emerging
issues in communications and networking. The issues include dynamic network
access, data rate control, wireless caching, data offloading, network security,
and connectivity preservation which are all important to next generation
networks such as 5G and beyond. Furthermore, we present applications of deep
reinforcement learning for traffic routing, resource sharing, and data
collection. Finally, we highlight important challenges, open issues, and future
research directions of applying deep reinforcement learning.Comment: 37 pages, 13 figures, 6 tables, 174 reference paper
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